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Optimization and Analysis of Wireless Networks Lifetime Using Soft Computing for Industrial Applications


Affiliations
1 SASTRA Deemed University, Thanjavur, Tamil Nadu 613 401, India
 

Recently, wireless networks are applied in various engineering and industrial applications. One of the critical problems in wireless network system optimization in intelligent applications is obtaining an adequate energy fairness level. This issue can be resolved by applying effective cluster-based routing optimization with multi-hop routing. Hence a new network structure is developed that is derived from energy consumption architecture by applying soft computing strategies such as evolutionary operators in determining the exact clusters for optimizing energy consumption. The new effective evolutionary operators are tested in the optimization of a lifetime. The proposed method is simulated for different values of the routing factor, α, for different types of networks. The energy levels range from 0.4 to 0.8, achieving good results for nearly 2500 rounds. The proposed strategy optimizes the clusters, and its head is selected reliably. The optimization of cluster head choice has been done based on the base station distance, the energy of the node, and the node's energy efficiency. The reliability of the long-distance nodes is increased during the data transmission by modifying the size of the area of the candidate set of nodes in contrast the near-distance node's energy consumption is reduced. For the energy levels that range from 0.4 to 0.8, the higher network throughput is obtained at the same time network lifetime is optimized compared to other well-known approaches. The proposed model is expected for different industrial wireless network applications to optimize the systems during the long-run simulation and to achieve high reliability and sustainability.

Keywords

Clustering, Energy Consumption, Evolutionary Modeling, Network Optimization, Throughput.
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  • Optimization and Analysis of Wireless Networks Lifetime Using Soft Computing for Industrial Applications

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Authors

N Muruganandam
SASTRA Deemed University, Thanjavur, Tamil Nadu 613 401, India
R Venkatesan
SASTRA Deemed University, Thanjavur, Tamil Nadu 613 401, India
Raja Marappan
SASTRA Deemed University, Thanjavur, Tamil Nadu 613 401, India
V Venkataraman
SASTRA Deemed University, Thanjavur, Tamil Nadu 613 401, India

Abstract


Recently, wireless networks are applied in various engineering and industrial applications. One of the critical problems in wireless network system optimization in intelligent applications is obtaining an adequate energy fairness level. This issue can be resolved by applying effective cluster-based routing optimization with multi-hop routing. Hence a new network structure is developed that is derived from energy consumption architecture by applying soft computing strategies such as evolutionary operators in determining the exact clusters for optimizing energy consumption. The new effective evolutionary operators are tested in the optimization of a lifetime. The proposed method is simulated for different values of the routing factor, α, for different types of networks. The energy levels range from 0.4 to 0.8, achieving good results for nearly 2500 rounds. The proposed strategy optimizes the clusters, and its head is selected reliably. The optimization of cluster head choice has been done based on the base station distance, the energy of the node, and the node's energy efficiency. The reliability of the long-distance nodes is increased during the data transmission by modifying the size of the area of the candidate set of nodes in contrast the near-distance node's energy consumption is reduced. For the energy levels that range from 0.4 to 0.8, the higher network throughput is obtained at the same time network lifetime is optimized compared to other well-known approaches. The proposed model is expected for different industrial wireless network applications to optimize the systems during the long-run simulation and to achieve high reliability and sustainability.

Keywords


Clustering, Energy Consumption, Evolutionary Modeling, Network Optimization, Throughput.

References